Optimized Assembly Tests from Machine Learning Correlation Algorithms
We explore ways of developing more efficient x86 assembly tests which check for functional coverage. The current testing suite used by our sponsor contains redundancies caused by the pseudo randomly generated test files. These redundancies lead to wasted computational time and resources. In our solution, we analyze pseudo randomly generated tests with known functional coverage checks, or "bin hits," using machine learning. For the training phase, our algorithm looks for common sequences of assembly instructions within tests that hit a given bin.